Image-based vehicle classification system

ABSTRACT

An image-based vehicle classification system includes a camera and an image server connected to the camera. The camera captures images of a road to result in an image stream. The image server includes a processor for receiving the image stream from the camera. For each of the images of the image stream, the processor performs image segmentation, a thinning process, an erosion process and a dilation process, and classifies, by a neural network classifier, a vehicle image part contained in the image into one of a large-size car class, a passenger car class and a motorcycle class when it is determined that the vehicle image is crossing an imaginary line set in advance in the image for counting vehicles.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims priority of Taiwanese Invention PatentApplication No. 106127881, filed on Aug. 17, 2017.

FIELD

The disclosure relates to a vehicle classification system and a vehicleclassification method, and more particularly to an image-based vehicleclassification system and an image-based vehicle classification method.

BACKGROUND

A conventional approach of measuring traffic flow, according to whichtraffic lights are to be controlled and coordinated to ensure safe andsmooth traffic, is realized by counting vehicles manually on a road.However, the conventional approach is relatively inefficient andlabor-intensive.

SUMMARY

Therefore, an object of the disclosure is to provide an image-basedvehicle classification system and an image-based vehicle classificationmethod that can alleviate at least one of the drawbacks of the priorart.

According to one aspect of the disclosure, the image-based vehicleclassification system includes a camera and an image server. The camerais configured to capture a series of images of a road to result in animage stream and to transmit the image stream. The image server iselectrically connected to the camera, and includes a communicationinterface and a processor.

The communication interface is configured to receive the image streamfrom the camera and to transmit the image stream. The processor iselectrically connected to the communication interface for receiving theimage stream from the communication interface. The processor isconfigured to, for each of the images of the image stream, perform imagesegmentation on the image so as to result in a background portion, and aforeground portion that includes a plurality of vehicle image partswhich respectively correspond to a plurality of vehicles. The processoris configured to perform, for each of the images of the image stream, athinning process on the foreground portion to result in a thinnedforeground portion. The processor is configured to perform, for each ofthe images of the image stream, an erosion process on the thinnedforeground portion to remove at least one connection line between anyoverlapping two of the vehicle image parts so as to result in an erodedforeground portion where the vehicle image parts are separated from eachother. The processor is configured to perform, for each of the images ofthe image stream, a dilation process on the vehicle images of the erodedforeground portion to result in a dilated foreground portion. Theprocessor is configured to determine, for each of the images of theimage stream, whether one of the vehicle image parts is crossing animaginary line set in advance in the image for counting vehicles. Theprocessor is configured to, for each of the images of the image stream,classify, by a neural network classifier when it is determined that oneof the vehicle image parts is crossing the imaginary line, the one ofthe vehicle image parts into one of a large-size car class, a passengercar class and a motorcycle class.

According to another aspect of the disclosure, the image-based vehicleclassification method is to be implemented by a system that includes acamera and an image server. The image server includes a communicationinterface and a processor. The image-based vehicle classification methodincludes following steps of:

-   -   by the camera, capturing a series of images of a road to result        in an image stream and transmitting the image stream;    -   by the communication interface, receiving the image stream from        the camera and transmitting the image stream;    -   receiving, by the processor, the image stream from the        communication interface; and    -   for each of the images of the image stream, by the processor,    -   performing image segmentation on the image so as to result in a        background portion, and a foreground portion that includes a        plurality of vehicle image parts which respectively correspond        to a plurality of vehicles,    -   performing a thinning process on the foreground portion to        result in a thinned foreground portion,    -   performing an erosion process on the thinned foreground portion        to remove at least one connection line between any overlapping        two of the vehicle image parts so as to result in an eroded        foreground portion where the vehicle image parts are separated        from each other,    -   performing a dilation process on the vehicle image parts of the        eroded foreground portion to result in a dilated foreground        portion,    -   determining whether one of the vehicle image parts is crossing        an imaginary line set in advance in the image for counting        vehicles, and    -   classifying, by using a neural network classifier when it is        determined that one of the vehicle image parts is crossing the        imaginary line, the one of the vehicle image parts into one of a        large-size car class, a passenger car class and a motorcycle        class.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features and advantages of the disclosure will become apparent inthe following detailed description of the embodiment with reference tothe accompanying drawings, of which:

FIG. 1 is a block diagram illustrating an embodiment of an image-basedvehicle classification system according to the disclosure;

FIG. 2 is a flow diagram illustrating an embodiment of an image-basedvehicle classification method according to the disclosure;

FIG. 3 is a schematic diagram illustrating an embodiment of displayingone image of an image stream;

FIG. 4 is a schematic diagram illustrating an embodiment of a neuralnetwork classifier according to the disclosure; and

FIG. 5 is a table exemplifying a result of vehicle counting obtained bythe embodiment of the image-based vehicle classification systemaccording to the disclosure.

DETAILED DESCRIPTION

Referring to FIG. 1, an embodiment of an image-based vehicleclassification system according to the disclosure is illustrated. Theimage-based vehicle classification system includes a camera 1 and animage server 2.

The camera 1 is configured to capture a series of images of a road toresult in an image stream and to transmit the image stream wirelessly tothe image server 2 based on mobile communication technology, such as thefourth generation of broadband cellular network technology.

The image server 2 is electrically connected to the camera 1, andincludes a communication interface 21, a processor 22, a memory 23 and adisplay 24.

The communication interface 21 is configured to receive the image streamfrom the camera 1 based on the mobile communication technology and totransmit the image stream to the processor 22.

The memory 23 is configured to store a software program 231 which isimplemented to utilize a neural network classifier 2310 to performimage-based vehicle classification.

The processor 22 is electrically connected to the memory 23, the display24, and the communication interface 21 for receiving the image streamfrom the communication interface 21. The processor 22 is configured toexecute the software program 231 stored in the memory 23 so as toperform, on each of the images of the image stream, the image-basedvehicle classification and vehicle counting. Details of the image-basedvehicle classification and vehicle counting will be described in thefollowing paragraphs.

The processor 22 is configured to perform image segmentation on each ofthe images of the image stream so as to result in a background portionand a foreground portion. The foreground portion includes a plurality ofvehicle image parts which correspond respectively to a plurality ofvehicles in the image. Among the vehicle image parts, at least onepartly overlaps another (e.g., see boxes containing numbers 94, 57 and102 in FIG. 3). In some cases, one or more of the vehicle image parts donot overlap with any other vehicle image part (e.g., see box containingnumber 142 in FIG. 3). In this disclosure, when two vehicle image partsare said to overlap, they may share a common edge (e.g., see boxescontaining numbers 94 and 102 in FIG. 3) or have an overlapping portion(e.g., see boxes containing numbers 52 and 102 in FIG. 3). It is notedthat any vehicle image part that overlaps with one or more other vehicleimage parts may also be referred to hereinafter as “overlapping vehicleimage part” and any vehicle image part that does not overlap with anyother vehicle image parts may also be referred to hereinafter as“non-overlapping vehicle image part”. In this embodiment, the imagesegmentation is implemented by the background reconstruction technique.However, implementation of the image segmentation is not limitedthereto. For instance, the image segmentation may be implemented by atleast one of the background reconstruction technique, a backgroundsubtraction technique or a moving object tracking technique.

In this embodiment, the background reconstruction technique is performedfor each of the images of the image stream based on a probability ofappearance p(y) of a pixel y in the image. The probability of appearancep(y) is calculated based on an equation of p(y′)=Σ_(j=1)^(K)ω_(j)G(y,μ_(j),Σ_(j)), where K represents a quantity of mixturecomponents in the image, ω_(j) represents an importance parameter of aj^(th) one of the mixture components, and G(y,μ_(j),Σ_(j)) represents amultivariate Gaussian distribution of the pixel y with mean u, andcovariance Σ_(j).

It is worth to note that for each of the images of the image stream, animage binarization threshold needs to be appropriately determined toperform image binarization on the image using the image binarizationthreshold thus determined so that the background portion and theforeground portion can be separated clearly from each other after imagesegmentation. However, determination of the image binarization thresholdby labor is time consuming. In this embodiment, the processor 22 of theimage server 2 is configured to perform statistical automaticthresholding algorithm (such as Otsu's method) so as to determine animage binarization threshold as the image undergoes image segmentation.

The processor 22 is configured to perform, for each image of the imagestream, a thinning process on the foreground portion to result in athinned foreground portion where boundaries between any overlapping pairof the overlapping vehicle image parts are thinned to become at leastone connection line so as to eliminate any existing overlapping betweenvehicle image parts, which hinders vehicle counting (i.e., counting ofvehicles present in one image of the image stream). As a result of thethinning process, the size of at least one of the vehicle image partsmay decrease.

The processor 22 is configured to perform, for each image of the imagestream, an erosion process on the thinned foreground portion to removesaid at least one connection line so as to result in an erodedforeground portion where the vehicle image parts are separated from eachother.

The processor 22 is configured to perform, for each image of the imagestream, a dilation process on the vehicle image parts of the erodedforeground portion to result in a dilated foreground portion where areasof the vehicle image parts are expanded to their original sizes as inthe image prior to the thinning process.

The processor 22 is configured to label the vehicle image parts anddetermine a width in pixels, a height in pixels, and an area that is theproduct of the width and the height of each of the vehicle image parts.Specifically speaking, in this embodiment, the processor 22 isconfigured to perform a row scan and/or a column scan on the dilatedforeground portion, and to assign different numbers in sequence torespective independent areas, which are sequentially detected in the rowscan and/or the column scan and which are represented in white colorafter the image binarization. The independent areas are separated fromeach other and represent respective vehicles. For example, a firstindependent area encountered while scanning is assigned a label one, anda second independent area encountered while scanning is assigned a labeltwo, and so forth.

In this embodiment, the processor 22 is configured to identify andspecify any of the vehicle image parts which has an area greater thanthirty pixels by a rectangular frame, and to tag the identified vehicleimage part by a number indicating the area thereof. Referring to anexample shown in FIG. 3, the vehicle image parts which have areasgreater than thirty pixels are specified by respective rectangularframes 241, 242, 243 and 244, and are tagged by respective numbers“142,” “94,” “102” and “57” for representing the areas of the vehicleimage parts.

The processor 22 is configured to determine, for each of the images ofthe image stream, whether one of the vehicle image parts is crossing animaginary line 240 (as shown in FIG. 3) set in advance in the image forcounting vehicles. In this embodiment, a vehicle image part whose centercoincides the imaginary line 240 is determined to be crossing theimaginary line 240.

The processor 22 is configured to classify, by the neural networkclassifier 2310 when it is determined for any of the images of the imagestream that one of the vehicle image parts thereof is crossing theimaginary line, the one of the vehicle image parts into one of alarge-size car class, a passenger car class and a motorcycle class. Itshould be noted that in this embodiment, the large-size car class andthe passenger car class are defined according to utilities; forinstance, the large-size car class includes truck or bus, and thepassenger car class includes a car for passengers. In other embodiments,they can be defined based on vehicle weight or vehicle dimensions. Theneural network classifier 2310 may be implemented by a convolutionalneural network (CNN) or a backpropagation neural network (BPN). In thisembodiment, the processor 22 is configured to determine, by imageprocessing, the width, the height and the area of each of the vehicleimage parts that is crossing the imaginary line 240, and to classify, bythe BPN, the vehicle image part into one of the large-size car class,the passenger car class and the motorcycle class.

Referring to FIG. 4, the BPN serves as the neural network classifier2310 and includes an input layer 2311 including three input parameters,an output layer 2313 including three output parameters, and at least onehidden layer 2312 between the input layer 2311 and the output layer2313. The input parameters include the width of the vehicle image part,the height of the vehicle image part, and the area of the vehicle imagepart. The output parameters include the large-size car class, thepassenger car class and the motorcycle class. In this embodiment, saidat least one hidden layer 2312 includes ten neurons, but implementationof a number of the neurons in the hidden layer 2312 is not limitedthereto. The BPN has to be trained in advance, before being utilized toperform real-time classification on the image stream, by a great numberof training samples each including a correspondence relationship betweenthe size (i.e., the width, the height and the area) of a vehicle imagepart and a class to which a vehicle corresponding to the vehicle imagepart actually belongs (i.e., one of the large-size car class, thepassenger car class and the motorcycle class).

It is worth to note that the BPN establishes nonlinear mapping betweeninputs and outputs through supervised learning. In this embodiment, anoutput vector Y can be obtained by Y=f(X*W), where X represents an inputvector, W represents a weight matrix, and f(⋅) is an activation functionand may be implemented by

${f(x)} = \frac{1}{1 + {\exp \left( {{- \alpha}\; x} \right)}}$

with a parameter α representing activity commonly used in the activationfunction of BPN. The parameter α may be set to 0.1.

Training of the BPN includes two phases, a feed-forward phase and aback-propagation phase. In the feed-forward phase where the weightmatrix is kept constant, the input parameters included in the inputvector are introduced into the input layer 2311, and then are weightedand summed at the hidden layer 2312, and are finally inputted into theactivation function to result in the output parameters included in theoutput vector which will be outputted at the output layer 2313.

In the back-propagation phase, the weight matrix is modified based on aresult of an error function that is calculated according to differencesbetween expected and actual values of the output vector. The expectedvalue may be, for example, the class to which a vehicle corresponding tothe vehicle image part actually belongs in the training sample. Theresult of the error function will be fed back to modify the weightmatrix when the result of the error function is outside of apredetermined range. Therefore, the BPN can be trained with the trainingsamples, and the weight matrix thereof can be consequently modified sothat the actual values of the output vector converge to the expectedvalues of the output vector.

The processor 22 is further configured to, after classifying the one ofthe vehicle image parts into one of the large-size car class, thepassenger car class and the motorcycle class, add one to a count ofvehicle image parts belonging to the one of the large-size car class,the passenger car class and the motorcycle class.

The display 24 is configured to display the image stream, and the countof vehicle image parts belonging to the large-size car class, the countof vehicle image parts the passenger car class and the count of vehicleimage parts the motorcycle class. For example, the counts of vehicleimage parts belonging to the classes are shown at an upper-left cornerof one of the images of the image stream displayed by the display 24 inFIG. 3, where letters “L,” “M” and “S” respectively represent thelarge-size car class, the passenger car class and the motorcycle class,and numbers following the letters respectively represent counts ofvehicle image parts belonging to the corresponding classes. In otherwords, it is indicated that ten passenger cars and seven motorcycleshave passed the imaginary line 240, while no large-size car has passedthe imaginary line 240 as exemplified in FIG. 3. In addition, a totalcount of vehicles that have passed the imaginary line 240 is shown at anupper-right corner of the image displayed by the display 24, such as anumber “17” shown in FIG. 3 which means a total of seventeen vehiclesbelonging to the three classes mentioned previously have passed theimaginary line 240.

FIG. 5 illustrates a table exemplifying a result of vehicle countinggenerated by the embodiment of the image-based vehicle classificationsystem according to the disclosure. The result of vehicle counting isassociated with traffic flow measured on a road in Taichung City on Jun.27, 2017, and Jun. 28, 2017. Counts of vehicles belonging to thelarge-size car class, the passenger car class and the motorcycle classare respectively 3, 328 and 415 at 6:15 on Jun. 27, 2017.

Referring to FIG. 2, an image-based vehicle classification methodaccording to the disclosure is illustrated. The image-based vehicleclassification method is to be implemented by the image-based vehicleclassification system mentioned above. The image-based vehicleclassification method includes steps S30-S39. Reference is further madeto FIG. 1.

In step S30, the camera 1 continues to captures images of a road toresult in the image stream and transmits the image stream to the imageserver 2. The communication interface 21 of the image server 2 receivesthe image stream from the camera 1 and transmits the image stream to theprocessor 22 of the image server 2 so as to enable the processor 22 toreceive the image stream from the communication interface 21.

A procedure including steps S31 to S39 is performed for each of theimages of the image stream.

In step S31, the processor 22 performs the image segmentation on theimage so as to result in the background portion and the foregroundportion. The foreground portion includes a plurality of vehicle imageparts which respectively correspond to a plurality of vehicles.

In step S32, the processor 22 performs the thinning process on theforeground portion to result in the thinned foreground portion.

In step S33, the processor 22 performs the erosion process on thethinned foreground portion to remove at least one connection linebetween any overlapping two of the vehicle image parts so as to resultin the eroded foreground portion where the vehicle image parts areseparated from each other.

In step S34, the processor 22 performs the dilation process on thevehicle image parts of the eroded foreground portion to result in thedilated foreground portion.

In step S35, the processor 22 labels the vehicle image parts anddetermines the width, the height and the area, all in pixels, of each ofthe vehicle image parts.

In step S36, the processor 22 determines whether one of the vehicleimage parts is crossing the imaginary line 240 set in advance in theimage for counting vehicles.

In step S37, the processor 22 classifies, by using the neural networkclassifier 2310 when it is determined that one of the vehicle imageparts is crossing the imaginary line 240, the one of the vehicle imageparts into one of the large-size car class, the passenger car class andthe motorcycle class.

In step S38, after classifying the one of the vehicle image parts intoone of the large-size car class, the passenger car class and themotorcycle class, the processor 22 adds one to the count of vehicleimage parts belonging to said one of the large-size car class, thepassenger car class and the motorcycle class.

In step S39, the display 24 displays the image stream, and the counts ofthe vehicle image parts belonging to the large-size car class, thepassenger car class and the motorcycle class.

In summary, by utilizing the image stream captured by the camera 1, theimage-based vehicle classification system and method according to thisdisclosure perform automatic classification and automatic counting onthe vehicle image parts in the image stream by the neural networkclassifier 2310. Therefore, automatic control of a traffic light can berealized based on the result of vehicle counting, saving manpower fortraffic management.

In the description above, for the purposes of explanation, numerousspecific details have been set forth in order to provide a thoroughunderstanding of the embodiment. It will be apparent, however, to oneskilled in the art, that one or more other embodiments may be practicedwithout some of these specific details. It should also be appreciatedthat reference throughout this specification to “one embodiment,” “anembodiment,” an embodiment with an indication of an ordinal number andso forth means that a particular feature, structure, or characteristicmay be included in the practice of the disclosure. It should be furtherappreciated that in the description, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure and aiding in theunderstanding of various inventive aspects.

While the disclosure has been described in connection with what isconsidered the exemplary embodiment, it is understood that thisdisclosure is not limited to the disclosed embodiment but is intended tocover various arrangements included within the spirit and scope of thebroadest interpretation so as to encompass all such modifications andequivalent arrangements.

What is claimed is:
 1. An image-based vehicle classification system,comprising: a camera configured to capture a series of images of a roadto result in an image stream and to transmit the image stream; and animage server electrically connected to said camera, and including acommunication interface configured to receive the image stream from saidcamera and to transmit the image stream, and a processor electricallyconnected to said communication interface for receiving the image streamfrom said communication interface, and configured to, for each of theimages of the image stream, perform image segmentation on the image soas to result in a background portion, and a foreground portion thatincludes a plurality of vehicle image parts which respectivelycorrespond to a plurality of vehicles, perform a thinning process on theforeground portion to result in a thinned foreground portion, perform anerosion process on the thinned foreground portion to remove at least oneconnection line between any overlapping two of the vehicle image partsso as to result in an eroded foreground portion where the vehicle imageparts are separated from each other, perform a dilation process on thevehicle image parts of the eroded foreground portion to result in adilated foreground portion, determine whether one of the vehicle imageparts is crossing an imaginary line set in advance in the image forcounting vehicles, and classify, by a neural network classifier when itis determined that one of the vehicle image parts is crossing theimaginary line, the one of the vehicle image parts into one of alarge-size car class, a passenger car class and a motorcycle class. 2.The image-based vehicle classification system as claimed in claim 1,wherein: said camera is configured to transmit the image streamwirelessly based on mobile communication technology; and saidcommunication interface of said image server is configured to receivethe image stream from said camera based on the mobile communicationtechnology.
 3. The image-based vehicle classification system as claimedin claim 1, wherein: said processor of said image server is configuredto perform at least one of a background subtraction technique, abackground reconstruction technique or a moving object trackingtechnique on each of the images of the image stream so as to result inthe background portion and the foreground portion for the image.
 4. Theimage-based vehicle classification system as claimed in claim 1,wherein: said processor of said image server is configured to, for eachof the images of the image stream, perform statistical automaticthresholding algorithm so as to determine an image binarizationthreshold, based on which image binarization is performed on the image,as image segmentation is performed on the image.
 5. The image-basedvehicle classification system as claimed in claim 1, wherein the neuralnetwork classifier is a convolutional neural network (CNN).
 6. Theimage-based vehicle classification system as claimed in claim 1, whereinthe neural network classifier is a backpropagation neural network (BPN).7. The image-based vehicle classification system as claimed in claim 6,wherein: the BPN includes an input layer including three inputparameters, an output layer including three output parameters, and atleast one hidden layer between the input layer and the output layer, theinput parameters including a width of the vehicle image part, a heightof the vehicle image part, and an area of the vehicle image part that isthe product of the width and the height of the vehicle image part, theoutput parameters including the large-size car class, the passenger carclass and the motorcycle class.
 8. The image-based vehicleclassification system as claimed in claim 7, wherein said at least onehidden layer includes ten neurons.
 9. The image-based vehicleclassification system as claimed in claim 1, wherein said processor isfurther configured to, after classifying the one of the vehicle imageparts into one of the large-size car class, the passenger car class andthe motorcycle class, add one to a count of vehicle image parts whichhave been classified as said one of the large-size car class, thepassenger car class and the motorcycle class.
 10. The image-basedvehicle classification system as claimed in claim 9, further comprising:a display electrically connected to said processor, and configured todisplay the image stream, and the counts of vehicle image partsbelonging to the large-size car class, the passenger car class and themotorcycle class.
 11. An image-based vehicle classification method to beimplemented by a system that includes a camera and an image server, theimage server including a communication interface and a processor, theimage-based vehicle classification method comprising steps of: by thecamera, capturing a series of images of a road to result in an imagestream, and transmitting the image stream; by the communicationinterface, receiving the image stream from the camera and transmittingthe image stream; receiving, by the processor, the image stream from thecommunication interface; and for each of the images of the image stream,by the processor, performing image segmentation on the image so as toresult in a background portion, and a foreground portion that includes aplurality of vehicle image parts which respectively correspond to aplurality of vehicles, performing a thinning process on the foregroundportion to result in a thinned foreground portion, performing an erosionprocess on the thinned foreground portion to remove at least oneconnection line between any overlapping two of the vehicle image partsso as to result in an eroded foreground portion where the vehicle imageparts are separated from each other, performing a dilation process onthe vehicle image parts of the eroded foreground portion to result in adilated foreground portion, determining whether one of the vehicle imageparts is crossing an imaginary line set in advance in the image forcounting vehicles, and classifying, by using a neural network classifierwhen it is determined that one of the vehicle image parts is crossingthe imaginary line, the one of the vehicle image parts into one of alarge-size car class, a passenger car class and a motorcycle class. 12.The image-based vehicle classification method as claimed in claim 11,wherein: the transmitting the image stream includes wirelesslytransmitting, by the camera, the image stream based on mobilecommunication technology; and the receiving the image stream includesreceiving, by the communication interface of the image server, the imagestream from the camera based on the mobile communication technology. 13.The image-based vehicle classification method as claimed in claim 11,wherein: the performing image segmentation includes performing, by theprocessor of the image server, at least one of a background subtractiontechnique, a background reconstruction technique or a moving objecttracking technique on the image so as to result in the backgroundportion and the foreground portion.
 14. The image-based vehicleclassification method as claimed in claim 11, wherein: the performingimage segmentation includes performing, by the processor of the imageserver, statistical automatic thresholding algorithm so as to determinean image binarization threshold, based on which image binarization isperformed on the image during performing image segmentation on theimage.
 15. The image-based vehicle classification method as claimed inclaim 11, wherein the neural network classifier is a convolutionalneural network (CNN).
 16. The image-based vehicle classification methodas claimed in claim 11, wherein the neural network classifier is abackpropagation neural network (BPN).
 17. The image-based vehicleclassification method as claimed in claim 16, wherein: the BPN includesan input layer including three input parameters, an output layerincluding three output parameters, and at least one hidden layer betweenthe input layer and the output layer, the input parameters including awidth of the vehicle image part, a height of the vehicle image part, andan area of the vehicle image part that is the product of the width andthe height of the vehicle image part, the output parameters includingthe large-size car class, the passenger car class and the motorcycleclass.
 18. The image-based vehicle classification method as claimed inclaim 17, wherein said at least one hidden layer includes ten neurons.19. The image-based vehicle classification method as claimed in claim11, further comprising, after classifying the one of the vehicle imageparts into one of the large-size car class, the passenger car class andthe motorcycle class, a step of: adding one to a count of vehicle imageparts belonging to said one of the large-size car class, the passengercar class and the motorcycle class.
 20. The image-based vehicleclassification method as claimed in claim 19, further comprising:displaying, by a display electrically connected to the processor, theimage stream, and the counts of the vehicle images belonging to thelarge-size car class, the passenger car class and the motorcycle class.